Grudon: A System for Deploying Graph Workloads on Disaggregated Architectures with Near-Data Processing

Emerging memory disaggregation and near-data processing (NDP) technologies have shown promise in addressing performance, scaling and efficiency challenges for data-intensive workloads such as graph analytics. However, they expose new tradeoffs related to data movement, runtime, and cost. This paper introduces Grudon, a novel system for deploying graph workloads on Disaggregated NDP (DiNDP) platforms. Grudon combines the benefits of memory disaggregation and NDP to tackle bandwidth limitations, improve resource flexibility, and reduce energy demands to improve overall workload performance. To achieve this, Grudon provides support for dynamic configuration of graph workload deployments on DiNDP platforms via new control plane functionality that enables lightweight evaluation of the tradeoff space and dynamic reconfiguration of operations executed on the different DiNDP elements. Evaluation on an emulated DiNDP testbed demonstrates that Grudon achieves a geomean speedup of 2.12× while reducing energy consumption by 3.3× compared to state-of-the-art distributed runtimes. These results show the potential to unlock the full capabilities of DiNDP platforms for efficient distributed graph analytics.